The Most Common Big Data Mistakes to Avoid
In recent years, many companies have started leveraging the benefits of big data to stay one step ahead of their competitors, and as a business owner, it’s hard not to be tempted by the benefits promised by big data.
But if you’re planning to leverage big data to grow your business, be sure to prepare for the many challenges it offers as well. If you’re able to navigate these challenges, you’ll find that big data really can help to propel your business forward into the future.
In this article, we’ll take a look on the most common challenges posed by big data and how to manoeuvre past them.
Not having clear goals
You are not going to find a solution to a problem that is unknown to you. Before considering big data as the saviour of your company’s fortunes, figure out what the problems are. Unless you know what you’re looking for, big data won’t be of much help to you. Once you know the problem, choosing the data sources will become a much simpler task so you’ll be more likely to use big data to your advantage. When you don’t know what you’re looking for, you’ll be combing through a lot of data but with poor results.
Looking at the wrong data
Big data is defined as data that comes from a multitude of sources. And with that comes one of the biggest challenges for business owners – data sources that are actually meaningful and insightful. Choosing the wrong data source can create a lot of confusion and lead to poor decision-making. Decisions made by consulting the wrong data source can be positively disastrous for an organisation, so special care should be given in this regard. Big data can be fed through a lot of data sources such as website analytics, social media, hardware sensors, machine logs, business apps, transactions, and so on. Choosing the right data source means finding out what matters to your business and then analysing it with the help of big data technology.
Failing to categorise data
Before you refine data, make sure to always categorise it at the early stages. Categorising data at later stages becomes an increasingly complex and resource-heavy task, one which can actually be counter-productive for the organisation as a whole. So before proceeding with anything, make sure that your data is categorised correctly for example by date, department, location, and so on. Once data has been categorised, it’s much easier to sort through it when the need for analytics arises. It also makes your big data system much easier to use as you’ll be able to drill down any category without much hassle.
Relying on poor data quality
A big data system is only as good as the data it’s being fed. If the data you capture is full of inaccurate information or just incomplete, it will lead to highly misleading insights gained by your big data system. This is the reason why large organisations have dedicated employees who’re responsible for ensuring the completeness and quality of data that the organisation’s systems capture. There are also ways with which seemingly ‘dirty’ data can be cleaned and made uniform. Once your big data system is being fed high quality data, there’s more likelihood that the insights it will offer will be much more accurate and useful.
Failing to break data down
The vast nature of big data can be overwhelming if you’re not experienced at handling it. Data should always be broken down into smaller pieces so that it becomes easier to understand and actionable. Always try to organise data in a way that makes it easier to understand and see through. You don’t want data to be clouding your judgment, rather it should assist in making clear decisions.
Failing to make use of the cloud
Big data is hugely resource-intensive. It requires a lot of processing power and storage space to truly be advantageous and productive for an organisation. Depending on the amount of data your organisation generates and collects, the infrastructure costs for setting up a big data system can be quite intimidating.
When faced with this challenge, many will rather go with a compromised and constrained big data system in the pursuit of lower infrastructure costs. A wiser move would be to move your data and processing to the cloud, where your big data system will work to its full potential. Going with the cloud means you don’t have to bear the upfront cost of setting up your own hardware infrastructure, while instantly giving you the power you need.
What’s more, as the requirements of your organisation change, you can either scale up or down your big data effort accordingly by simply upgrading/downgrading your cloud storage plan.
How to Succeed
When used wisely and effectively, Big Data can bring unprecedented levels of growth for organisations.
Big data will be the deciding factor for many organisations with regards to their success. Data has always had importance for business but with big data, it has gained even more power over the future of an organisation.
In the right hands, big data can work wonders, but in inexperienced hands, it can wreak havoc for an organisation. To make sure your big data efforts don’t fail, make sure to hire experienced big data scientists and consultants, who are adept at solving the challenges posed by huge amounts of data.